Why Promote Improved Fallows as a Climate-Smart Agroforestry Technology in Sub-Saharan Africa?
Bibliographic record
Abstract
In the literature, a lot is discussed about how agroforestry can achieve the mitigation, adaptation and productivity goals of climate-smart agriculture (CSA). However, this may be relatively too broad to assess the trade-offs and synergies of how specific agroforestry technologies or practices achieve the three pillars of CSA. Here, we provide an overview of how improved fallows (an agroforestry technology consisting of planting mainly legume tree/shrub species in rotation with cultivated crops) may achieve the goals of climate-smart agriculture in Sub-Saharan Africa (SSA). Our review showed that improved fallow systems have real potential to contribute to food security and climate change mitigation and adaptation in SSA. Under proper management, improved fallows can increase maize yields to about 6 t ha−1, which is comparable to conventional maize yields under fertilization. This is attributed to improved soil fertility and nutrient use efficiency. Although data was generally limited, the growing literature showed that improved fallows increased soil carbon sequestration and reduced greenhouse emissions. Further, as a multiple output land use system, improved fallows may increase fodder availability during dry periods and provide substantial biomass for charcoal production. These livelihood options may become important financial safety nets during off seasons or in the event of crop failures. This notwithstanding, the adoption of improved fallows is mainly in Southern and Eastern Africa, where over 20,000 farmers are now using Sesbania sesban, Tephrosia vogelii, and Cajanus cajan in two-year fallows followed by maize rotations. Land tenure issues, lack of social capital, and improved germplasm and accessions of fallow species have been cited as constraints to scaling up. However, development of seed orchards, nursery development, and the willingness of policy makers to create a policy environment that addresses market failures and alleviates disincentives should improve adoption and future scaling up.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".